Introduction to Geophysical Imaging
Bibliographic record
Abstract
As we have seen from the preceeding chapters, it has become widely accepted that straightforward gas-hydrate assessment remains an outstanding issue. Researchers no longer attempt to gauge marine gas-hydrate concentrations from seismic reflectivity maps of the bottom-simulating reflections (BSRs), and rarely can a land-based equivalent to a marine BSR be clearly identified because of the geologic complexity of the permafrost gas-hydrate environment. In fact, there is increasing evidence that gas-hydrate deposits in the marine environment are very heterogeneous in nature, especially from the last major scientific drilling expeditions (e.g., Integrated Ocean Drilling Program Expedition 311, Riedel et al., 2006; India National Gas Hydrate Expedition 01, Collett et al., 2008). Considerable strides have been made to develop more sophisticated geophysical experimental methodologies, inversions, and gas-hydrate assessment methods. The need to employ other geophysical imaging techniques has become more and more evident.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.057 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".